Ollama, LM Studio, and GPT4All Are All Just llama.cpp — Here's Why Performance Still Differs
When running local LLMs on an RTX 4060 8GB, the first decision isn't the model. It's the framework.
llama.cpp, Ollama, LM Studio, vLLM, GPT4All — plenty of options. But under an 8GB VRAM constraint, the framework choice directly affects inference speed. A 0.5GB difference in overhead changes which models you can load at all. One extra API abstraction layer adds a few ms of latency.
What follows is a comparison on identical hardware with identical models.
Frameworks and Evaluation Criteria
Framework Overview
frameworks = {
"llama.cpp (CLI)": {
"version": "b8233 (2026-03)",
"backend": "CUDA + Metal + CPU",
"quantization": "GGUF (Q2_K ~ FP16)",
"API": "CLI / llama-server (OpenAI-compatible)",
"strength": "Minimal overhead, maximum control",
},
"Ollama": {
"version": "0.6.x",
"backend": "llama.cpp (bundled)",
"quantization": "GGUF (via Ollama Hub)",
"API": "REST API + CLI",
"strength": "Docker-like simplicity, easy model management",
},
"LM Studio": {
"version": "0.3.x",
"backend": "llama.cpp (bundled)",
"quantization": "GGUF (GUI search)",
"API": "OpenAI-compatible API + GUI",
"strength": "GUI, beginner-friendly, function calling support",
},
"vLLM": {
"version": "0.7.x",
"backend": "Custom CUDA kernels + PagedAttention",
"quantization": "AWQ, GPTQ, FP8, GGUF (v0.4.2+)",
"API": "OpenAI-compatible API",
"strength": "Batch processing optimization, server-oriented",
},
"GPT4All": {
"version": "3.x",
"backend": "llama.cpp (bundled)",
"quantization": "GGUF",
"API": "GUI + Python SDK",
"strength": "Simplest setup, offline-first",
},
}
The critical fact: Ollama, LM Studio, and GPT4All all use llama.cpp internally. The differences are purely in wrapper design. Only vLLM has its own CUDA kernels.
Evaluation Axes
evaluation_axes = {
"Inference speed (t/s)": "Generation speed with identical model and quantization",
"VRAM overhead": "VRAM consumed by the framework itself, excluding the model",
"Cold start time": "Time to complete model loading",
"API compatibility": "OpenAI API compatibility and quality",
"Function calling": "Tool-use support and accuracy",
"Setup difficulty": "Steps from install to first inference",
}
Inference Speed Comparison
Test Conditions
test_config = {
"GPU": "RTX 4060 Laptop (8GB VRAM)",
"model": "Qwen2.5-7B-Instruct Q4_K_M (4.7GB)",
"prompt": "Explain the difference between TCP and UDP in 200 words",
"max_tokens": 256,
"temperature": 0.7,
"context_length": 4096,
"measurement": "Median of 3 runs",
}
Results
Framework Prompt eval Generation TTFT VRAM overhead
(t/s) (t/s) (ms) (excl. model)
────────────────────────────────────────────────────────────────
llama.cpp (CLI) ~800 32.1 120 ~0.3 GB
llama-server ~780 31.5 135 ~0.4 GB
Ollama ~750 30.2 180 ~0.5 GB
LM Studio ~720 29.8 250 ~0.6 GB
GPT4All ~680 28.5 300 ~0.7 GB
vLLM N/A* N/A* N/A* ~1.5 GB+
* vLLM OOM with default settings on 8GB VRAM
(PagedAttention KV cache pre-allocation consumes additional VRAM)
Analysis
speed_analysis = {
"llama.cpp vs Ollama": {
"gap": "32.1 vs 30.2 = 5.9%",
"cause": "Ollama's REST API layer + model management daemon overhead",
"practical_impact": "Negligible. Convenience offsets the difference.",
},
"llama.cpp vs LM Studio": {
"gap": "32.1 vs 29.8 = 7.2%",
"cause": "GUI + additional API abstraction layers",
"practical_impact": "GUI benefits outweigh speed loss for most use cases",
},
"llama.cpp vs GPT4All": {
"gap": "32.1 vs 28.5 = 11.2%",
"cause": "Python SDK overhead + non-optimized default settings",
"practical_impact": "Acceptable for beginners, room for optimization",
},
"vLLM": {
"issue": "Cannot run 7B models on 8GB VRAM",
"cause": "PagedAttention KV cache pre-allocation consumes additional VRAM",
"use_case": "Tunable via gpu_memory_utilization, but practically needs 16GB+",
},
}
# Bottom line: llama.cpp is fastest, but the gap is 6-11%
# On 8GB VRAM, the real differentiator is overhead (0.3GB vs 1.5GB)
# That overhead gap determines your maximum model size
When VRAM Overhead Becomes Fatal on 8GB
On 8GB VRAM, framework overhead directly dictates your maximum model size.
# Maximum model size per framework
max_model_size = {
"llama.cpp": {
"overhead": 0.3,
"cuda_context": 0.3,
"available_for_model": 8.0 - 0.3 - 0.3, # 7.4 GB
"max_model": "Qwen2.5-32B Q4_K_M (18GB) -> 7.4GB on GPU + 10.6GB CPU offload",
"max_full_gpu": "Mistral-Nemo-12B Q4_K_M (7.2GB) -> barely fits",
},
"Ollama": {
"overhead": 0.5,
"cuda_context": 0.3,
"available_for_model": 8.0 - 0.5 - 0.3, # 7.2 GB
"max_full_gpu": "7B Q4_K_M (4.7GB) -> comfortable, 12B -> tight",
},
"LM Studio": {
"overhead": 0.6,
"cuda_context": 0.3,
"available_for_model": 8.0 - 0.6 - 0.3, # 7.1 GB
"max_full_gpu": "7B Q4_K_M (4.7GB) -> comfortable, 12B -> difficult",
},
"vLLM": {
"overhead": 1.5,
"cuda_context": 0.3,
"available_for_model": 8.0 - 1.5 - 0.3, # 6.2 GB
"max_full_gpu": "Even 7B models have no headroom",
"note": "Not recommended for 8GB",
},
}
The overhead difference between llama.cpp and vLLM is 1.2GB. That 1.2GB could buy you:
- Additional KV cache allocation to extend context length
- Room to co-locate a BGE-M3 embedding model alongside your LLM
- Higher GPU offload ratio for the model, speeding up inference
On 8GB VRAM, framework selection isn't a preference. It's an architectural decision.
Function Calling Support
As covered in my function calling article (separate article), tool use is the killer feature for local LLMs. Here's where each framework stands:
function_calling_support = {
"llama.cpp (llama-server)": {
"supported": True,
"method": "OpenAI-compatible tools parameter",
"GBNF_grammar": True, # Enforces JSON output grammatically
"quality": "Model-dependent. High accuracy with Qwen2.5-7B-Instruct + GBNF grammar",
"limitation": "Requires manual server startup",
},
"Ollama": {
"supported": True,
"method": "OpenAI-compatible tools parameter (v0.4+)",
"GBNF_grammar": False, # No raw GBNF, but format parameter supports JSON Schema
"quality": "Same as llama.cpp (identical backend)",
"limitation": "No GBNF grammar, but structured output via format parameter with JSON Schema",
},
"LM Studio": {
"supported": True,
"method": "OpenAI-compatible tools parameter",
"GBNF_grammar": True, # JSON Schema enforcement
"quality": "Testable through GUI, which is the main advantage",
"limitation": "Backend equivalent to llama.cpp",
},
"vLLM": {
"supported": True,
"method": "OpenAI-compatible tools + Guided Decoding",
"quality": "High accuracy via Guided Decoding",
"limitation": "Needs gpu_memory_utilization tuning on 8GB, practically 16GB+ recommended",
},
"GPT4All": {
"supported": False,
"note": "No function calling support. Chat only.",
},
}
GPT4All doesn't support function calling. It's unusable for agentic workflows. vLLM's Guided Decoding is powerful but impractical on 8GB. For function calling on 8GB VRAM, you're limited to the llama.cpp family -- direct, Ollama, or LM Studio.
Recommendations by Use Case
recommendations = {
"Maximum performance (developers)": {
"pick": "llama.cpp (CLI / llama-server)",
"reasons": [
"Minimal overhead (0.3GB)",
"GBNF grammar enforces structured output",
"Direct control over all parameters",
"Per-layer GPU/CPU offload granularity",
],
"downside": "Requires technical knowledge, no GUI",
},
"Convenient daily use": {
"pick": "Ollama",
"reasons": [
"Docker-pull simplicity (ollama pull model)",
"Background daemon, always available",
"OpenAI-compatible API for drop-in replacement",
"Within 6% of llama.cpp speed",
],
"downside": "No GBNF grammar (JSON Schema via format param available), slightly larger overhead",
},
"GUI-driven experimentation": {
"pick": "LM Studio",
"reasons": [
"Model search and download entirely in GUI",
"Chat UI for real-time testing",
"Function calling testable through the interface",
],
"downside": "Higher memory footprint due to GUI layer",
},
"Easiest possible start (non-engineers)": {
"pick": "GPT4All",
"reasons": [
"Install -> launch -> chat in minimal steps",
"Fully offline",
"No unnecessary configuration options",
],
"downside": "No function calling, slowest speed, limited customization",
},
"Production / server deployment": {
"pick": "vLLM (16GB+ GPU recommended) or llama-server",
"reasons": [
"vLLM: PagedAttention for efficient batch processing",
"llama-server: Lightweight server that works on 8GB",
],
"downside": "vLLM impractical on 8GB",
},
}
The Verdict for 8GB
Question: What's the optimal framework for 8GB VRAM?
Answer: Depends on use case. But technically optimal is raw llama.cpp.
Why:
1. Minimum overhead (0.3GB) -> maximum usable VRAM
2. Fastest speed (+6-11% over other frameworks)
3. GBNF grammar enforces structured output -> highest function calling reliability
4. Per-layer GPU/CPU offload control
However:
- For daily use, Ollama's convenience outweighs the speed gap
- If you need a GUI, LM Studio is the only option
- vLLM is impractical on 8GB (needs 16GB+)
- GPT4All is unsuitable for agentic tasks (no function calling)
The total speed spread across all frameworks is within 11%.
Model selection matters far more than framework selection.
The gap between Qwen2.5-3B (2.0GB) and Qwen2.5-7B (4.7GB)
dwarfs the gap between llama.cpp and GPT4All.
If you're spending time agonizing over frameworks, spend it benchmarking models instead.
References
- llama.cpp -- github.com/ggerganov/llama.cpp
- Ollama -- ollama.ai
- LM Studio -- lmstudio.ai
- vLLM -- vllm.ai
- GPT4All -- gpt4all.io
- "Efficient Memory Management for Large Language Model Serving with PagedAttention" (2023) arXiv:2309.06180
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